Xiao Guangwei, Ding Jianyu, Shao Shizhou, Wang Lin, Gao Lei, Luo Xiaohua, Wei Zhaozhao, Tan Xiaohong, Guo Jie, Qian Jiangjin, Xiao Anhong, Wang Jiahua
China Tobacco Hubei Industrial Co., Ltd, Wuhan 430000, Hubei, China.
College of Food Science and Engineering, Wuhan Polytechnic University, Wuhan 430023, Hubei, China.
Heliyon. 2024 Jul 25;10(15):e35178. doi: 10.1016/j.heliyon.2024.e35178. eCollection 2024 Aug 15.
Alcoholization is an integral part of tobacco processing and volatile compounds are key to assessing tobacco alcoholization. In this study, a total of 154 volatiles from nine categories were determined by gas chromatography-ion mobility spectrometry (GC-IMS) from four grades of tobacco, of which 114 were better identified. And then, the dynamic trends of volatile compounds with significant changes in tobacco alcoholization were analyzed. The relevant volatiles with the alcoholization indices (AIs) (R > 0.8) were screened as indicators of tobacco alcoholization. Cinnamyl isobutyrate, linolenic acid alcohol, propanoic acid-M and propanoic acid-D in all tobacco samples were highly correlated with the AIs and tended to increase during the alcoholization process. In addition, linear discriminant analysis (LDA), back-propagation neural network (BPNN) and random forest (RF) classifiers were constructed for discrimination of tobacco AIs. Three classifiers trained with a combination of 20 volatiles achieved satisfactory results with area under the curve (AUC) of 0.95 (LDA), 0.94 (BPNN) and 0.97 (RF), respectively. The RF classifier gained optimal accuracy of 100 % and 96.1 % for the training and test sets, respectively. The study confirmed that GC-IMS can be used to characterize the changes of volatile compounds in tobacco during alcoholization and combined with machine learning to achieve the determination of AIs. The results of the study may provide a new means for the tobacco industry to monitor the alcoholization process and determine the degree of alcoholization.
醇化是烟草加工的一个重要组成部分，挥发性化合物是评估烟草醇化的关键。在本研究中，采用气相色谱-离子迁移谱（GC-IMS）从四个等级的烟草中测定了九大类共154种挥发性化合物，其中114种得到了较好的鉴定。然后，分析了烟草醇化过程中挥发性化合物的动态变化趋势。筛选出醇化指数（AIs）（R>0.8）相关的挥发性化合物作为烟草醇化的指标。所有烟草样品中的异丁酸肉桂酯、亚麻酸醇、丙酸-M和丙酸-D与醇化指数高度相关，且在醇化过程中呈上升趋势。此外，构建了线性判别分析（LDA）、反向传播神经网络（BPNN）和随机森林（RF）分类器用于判别烟草醇化指数。用20种挥发性化合物组合训练的三个分类器分别取得了满意的结果，曲线下面积（AUC）分别为0.95（LDA）、0.94（BPNN）和0.97（RF）。RF分类器在训练集和测试集上的最佳准确率分别为100%和96.1%。该研究证实GC-IMS可用于表征烟草醇化过程中挥发性化合物的变化，并结合机器学习实现醇化指数的测定。研究结果可为烟草行业监测醇化过程和确定醇化程度提供新的手段。
